Daniel Watson
Computer Vision and Pattern Recognition · 2021–2022
A researcher in the Science corpus: 3 papers; trusts 233 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 84 citations into it; #10,314 of 21,575 by trust here, #8,409 by citations
- Generative Models — 68 citations into it; #956 of 1,956 by trust here, #949 by citations
- Artificial Intelligence — 43 citations into it; #21,385 of 35,345 by trust here, #17,110 by citations
- Statistical and Nonlinear Physics — 26 citations into it
- Probabilistic & Bayesian Methods — 16 citations into it
- Computer Graphics and Computer-Aided Design — 15 citations into it
- Signal Processing — 15 citations into it
- Computational Mechanics — 14 citations into it
Whom Daniel Watson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Jonathan Ho
- Alex Krizhevsky
- Yang Song
- Geoffrey E. Hinton
- Yoshua Bengio
- Prafulla Dhariwal
- Alex Nichol
- Pascal Vincent
- Kaiming He
Who trusts Daniel Watson overall
- Fan Bao
- Valentin De Bortoli
- Hanqun Cao
- Heewoo Jun
- Peng Wang
- Ling Yang
- Qinsheng Zhang
- Alex Nichol
- Tim Dockhorn
- Chongxuan Li
Papers from Daniel Watson's perspective
Ordered by how strongly the researchers Daniel Watson trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning to Efficiently Sample from Diffusion Probabilistic Models (2021) — 163 citations
- Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality (2022) — 241 citations
- Novel View Synthesis with Diffusion Models (2022) — 348 citations